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Results: 158
Number of items: 158
  • Open Access
    Eijkelboom, F., Zimmermann, H., Vadgama, S., Bekkers, E. J., Welling, M., Naesseth, C. A., & van de Meent, J.-W. (2025). Controlled Generation with Equivariant Variational Flow Matching. Proceedings of Machine Learning Research, 267, 15066-15078. https://proceedings.mlr.press/v267/eijkelboom25a.html
  • Open Access
    Löwe, S. (2024). Learning structured representations of objects and relations. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Romijnders, R., Louizos, C., Asano, Y. M., & Welling, M. (2024). Protect Your Score: Contact-tracing With Differential Privacy Guarantees. In M. Wooldridge, J. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence: AAAI-2024 (Vol. 13, pp. 14829-14837). AAAI Press. https://doi.org/10.1609/aaai.v38i13.29402
  • Open Access
    Wöhlke, J. G. (2024). Reinforcement learning and planning for autonomous agent navigation: With a focus on sparse reward settings. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    García Satorras, V. (2024). Inductive biases for graph neural networks. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Esmaeili, B. (2024). Learning useful representations with variational autoencoders. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Schneuing, A., Harris, C., Du, Y., Didi, K., Jamasb, A., Igashov, I., Du, W., Gomes, C., Blundell, T. L., Lio, P., Welling, M., Bronstein, M., & Correia, B. (2024). Structure-based drug design with equivariant diffusion models. Nature Computational Science, 4(12), 899-909. https://doi.org/10.1038/s43588-024-00737-x
  • Open Access
    Miller, B. K. (2024). Machine learning for scientific simulation: Inference and generative models. [Thesis, fully internal, Universiteit van Amsterdam].
  • Open Access
    Veeling, B. S. (2024). Deep learning for medical data. [Thesis, fully internal, Universiteitsbibliotheek].
  • Open Access
    Igashov, I., Stärk, H., Vignac, C., Schneuing, A., Satorras, V. G., Frossard, P., Welling, M., Bronstein, M., & Correia, B. (2024). Equivariant 3D-conditional diffusion model for molecular linker design. Nature Machine Intelligence, 6(4), 417–427. https://doi.org/10.1038/s42256-024-00815-9
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